1. No category

Vehicle positioning and route prediction
FFI resultatkonferens 2015-09-17
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
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VPRP Project work packages
Project duration
From: 2013-07-01
To:
2016-12-31
2015-09-17
WP2 System design
WP1 Project management
WP3 Local map
WP4 Vehicle
posi oning
Total project budget:
24,6 MSek
WP5 Route predic on
WP6 Global map
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
2
VCC Results for 2014-2015
Localization and
mapping in AD-pilot
• Focus on robust and high
performing localization (WP 3)
• Development of a high density map
on a dedicated AD-route (WP 6)
Localization and
mapping research
• Evaluation of localization
performance using today’s
standard sensors (WP 4.1)
• Using GNSS sensors of dead
reckoning (WP 4.2)
Destination estimation
and route prediction
•
•
Clustering driving destinations
(WP 5.2)
Short range route prediction
(WP 5.2)
System design
• Two fully equipped V60 test vehicles up and running (WP 2)
• Six XC90 vehicles under construction, ready after 15w52 (WP 2)
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
3
Robust localization (WP 3)
Compare:
• Landmarks detected by the sensor system ( , ) & landmarks stored in a HD-map
• Road shape estimated by the sensor system & road shape stored in a HD-map
Landmarks can consist of:
•
•
•
•
•
2015-09-17
Lane markings / road edges
Barriers
Traffic signs
Magnets
etc.
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
4
High density mapping
(WP 6)
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED,
VER1, PUBLIC
5
Example of map data
Video
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
6
Localization evaluation (WP4.1)
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
7
Comparison L1 GPS carries phase usage (WP 4.2)
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
8
Clustering driving destinations (WP 5.2)
Large similarities in trajectories from an origin
to a destination
Different parking locations diversities
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
9
Clustering driving destinations (WP 5.2)
Large similarities in trajectories from an origin
to a destination
A new method for clustering driving destinations
has been developed.
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
10
Short range route prediction (WP 5.2)
•
•
2015-09-17
The problem of short range (next
link) prediction has been studied
We have developed a new
method which has two main
benefits
• low real time computational
complexity
• the prediction model can be
sequentially updated for each
new trip
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
11
Chalmers
• Education:
• Courses:
• Probabilistic graphical models
• Sensor data fusion
• 2 Master thesis projects:
• “Monocular simultaneous localisation and mapping for road vehicles”
• “Simultaneous Localization and Mapping for Vehicle Localization using LIDAR
Sensors”
• 1 Bachelor thesis project:
Fig 1: Identified and classifiable
routes from data set.
• “Prediction and classification of driver’s route”
• Supervison:
• Supervision of 2 industrial PhD students (see VCC and AB Volvo slides)
• Research
• Radar sensor maps for improved localization.
Fig 2: SLAM with LIDAR data.
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
12
Chalmers – Radar sensor maps
• Positioning problem
• Use current sensor observations to position the vehicle in a map of known
(sensor specific) landmarks.
• Mapping problem
• Use recoded sensor data from the vehicle to build up an accurate map of
how the sensor sees the world. We call this map the sensor map.
• Why radar maps?
• Radars is an important sensor as they are robust against different weather
conditions.
• The radar sensor map
• Radar landmarks in the map are described with position, extension and
expected number of landmarks.
• The radar map is estimate from batch data both where we assume that the
host trajectory is known and when it is not (SLAM).
Fig 3: Schematic view of a radar sensor map.
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
13
AB Volvo Update
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
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SUMMARY VPRP – SO FAR
• In general good progress and result
• Accurate and depandable positioning is a fundamental, critical enabler for systems that relate to the long-term zero-accident
vision
• This research will have direct impact on our opportunities to reach targets on active safety and self-driving. We are at the
research frontier.
• Next time (Resultatkonferens) – more focus for partners Chalmers and AB Volvo
• VCC project setup:
•
•
•
•
Defined Target project AD Pilot
in combination with Research project VPRP
in combination with other Data Collection project running
Good mixture with synergies
Thank you for your time!
Next time - maybe in a running test vehicle (XC90)!
2015-09-17
FFI RESULTATKONFERENS 2015-09-17, ANDERS ALMEVAD & JOAKIM LIN-SÖRSTEDT, AALMEVA1 & JSORSTED, VER1, PUBLIC
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